Chapter 2 Natural Disaster and Economic Growth: Bridging the Short-
2.4 Results and Discussion on Disaster
2.4.2 Results by Disaster Categories and Types
Table 2.4 provides the results in terms of hydro-meteorological and geophysical disasters. In both disaster categories, NCAT disasters have positive effects on economic growth in the short term and medium term. On the other hand, the impacts of CAT disasters vary depending on the categories; severe hydro-meteorological disasters have negative impacts for all time frames, while we observe negative effects of geophysical disasters only in the medium term. The similarity between the results of hydro-meteorological disasters and the results of the aggregated sample in Table 2.3 is partly explained by the fact that hydro-meteorological disasters occur relatively more frequently than geophysical disasters. The positive effects of an NCAT hydro-meteorological disaster is consistent with the result of Skidmore and Toya (2002), which found a positive relationship between the frequency of NCAT disasters and growth in the case of climate-related disasters. Additionally, the result for CAT geophysical disasters is consistent with the negative relationship between geological disasters and economic growth found in Skidmore and Toya (2002).
Table 2.5 shows that the disaster impacts vary depending on the disaster types.
Models 1 and 2 examine the impacts of short-, medium- and long-term impacts of CAT and NCAT separately. The impacts of extreme temperature events, particularly
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Table 2.4 Estimation Results for Hydro-meteorological and Geophysical Disasters
Decision Rules of Severity not separated not separated 99% 99%
(1) (2) (3) (4)
Hydro-meteorological Disasters
Mean Effects (t - t-30) 0.036***
(0.012)
Short-run (t - t-5) 0.008*
(0.005)
Medium-run (t-6 - t-10) 0.016***
(0.004)
Long-run (t-11 - t-30) 0.010
(0.011)
CAT
Mean Effects (t - t-30) -0.390***
(0.106)
Short-run (t - t-5) -0.103***
(0.035)
Medium-run (t-6 - t-10) -0.067*
(0.038)
Long-run (t-11 - t-30) -0.207**
(0.084)
NCAT
Mean Effects (t - t-30) 0.035***
(0.012)
Short-run (t - t-5) 0.008*
(0.005)
Medium-run (t-6 - t-10) 0.013***
(0.004)
Long-run (t-11 - t-30) 0.012
(0.011) Geophysical Disasters
Mean Effects (t - t-30) 0.022
(0.024)
Short-run (t - t-5) 0.019**
(0.008)
Medium-run (t-6 - t-10) 0.021***
(0.008)
Long-run (t-11 - t-30) -0.019
(0.020)
CAT
Mean Effects (t - t-30) -0.243
(0.217)
Short-run (t - t-5) -0.075
(0.046)
Medium-run (t-6 - t-10) -0.107**
(0.049)
Long-run (t-11 - t-30) -0.102
(0.176)
NCAT
Mean Effects (t - t-30) 0.017
(0.024)
Short-run (t - t-5) 0.016**
(0.008)
Medium-run (t-6 - t-10) 0.015**
(0.008)
Long-run (t-11 - t-30) -0.014
(0.021)
Observations 3,232 3,232 3,232 3,232
Number of Countries 173 173 173 173
Adjusted R-squared 0.198 0.199 0.200 0.200
Note: ***, **, * denote significance at the 1%, 5%, and 10% level, respectively. Robust standard errors reported in parentheses. Controls, country and year fixed effects included but not reported.
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heat waves, are similar to the results of all disasters in Table 2.3 as well as the results of hydro-meteorological disasters in Table 2.4. CAT disasters have negative impacts on economic growth in the short and medium term. On the other hand, NCAT disasters have positive short- and medium-term impacts.
We find contrasting effects in the analyses of other disaster types, including floods, storms, earthquakes, and droughts. For floods, NCAT events have positive impacts in medium and long term, which is consistent with previous studies on the impacts of
Table 2.5 Estimation Results by Each Disaster Type
Extreme
Temperature Flood Storm Earthquake Drought Model 1
CAT
Mean Effects (t - t-30) -1.353*** 0.162 -0.384** -0.587* -0.486 (0.245) (0.595) (0.193) (0.304) (0.820) NCAT
Mean Effects (t - t-30) 0.113*** 0.0521*** -0.115*** 0.0265 0.199***
(0.0415) (0.0184) (0.0213) (0.0332) (0.0633)
Observations 3,232
Adjusted R-squared 0.205
Model 2 CAT
Short-run (t - t-5) -0.206*** 0.022 -0.056 -0.105** omitted (0.045) (0.158) (0.065) (0.046)
Medium-run (t-6 - t-10) -0.285*** 0.177 0.016 -0.121** -0.138 (0.049) (0.125) (0.067) (0.052) (0.121) Long-run (t-11 - t-30) omitted -0.282 -0.141 0.027 -0.366 (0.326) (0.114) (0.194) (0.278) NCAT
Short-run (t - t-5) 0.0200** 0.008 -0.021*** 0.016* 0.009 (0.008) (0.005) (0.006) (0.009) (0.013) Medium-run (t-6 - t-10) 0.0175* 0.012** -0.013** 0.018** 0.037***
(0.010) (0.005) (0.006) (0.008) (0.012) Long-run (t-11 - t-30) 0.001 0.030* -0.062*** -0.001 0.128***
(0.034) (0.016) (0.013) (0.025) (0.046)
Observations 3,232
Adjusted R-squared 0.206
Note: ***, **, * denote significance at the 1%, 5%, and 10% level, respectively. Robust standard errors reported in parentheses. Controls, country and year fixed effects included but not reported.
Landslides, wildfires and volcanic eruptions also included but not reported in this Table, due to zero or very small observations.
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floods on economic growth (Cunado and Ferreira, 2014; Fomby et al., 2013).
Storms have negative economic impacts for both CAT and NCAT events according to Model 1. In Model 2, however, we find impacts only for NCAT events.
Although an average number of CAT storms in the past 30 years negatively affected economic growth, we find no statistically significant impact of CAT storms when we examine separate time frames. On the other hand, a storm is only considered a disaster type when NCAT events have statistically significant negative impacts on economic growth, and this result is consistent with the findings of Hsiang and Jina (2014) that showed short- and long-term negative impacts of storms through a 20-year period.
In the case of earthquakes, our results show that the CAT earthquakes have short- and medium-term negative impacts on economic growth but no long-term impacts. On the other hand, we find that NCAT earthquakes have a positive impact on economic growth in the short and medium terms, and the initial recovery process with relief funds that follow an earthquake to rebuild residential housing, public infrastructure, and industrial plants may explain the positive impact (Fomby et al., 2013).
We find negative impacts of storms and earthquakes, the negative impacts of earthquakes increase through a medium time frame, and the impacts of storms remain for an extensive period and have larger impacts over the long term. These differences between the effects of different disaster categories may be explained by the difference in the likelihood of storm and earthquake occurrences. Severe earthquakes tend not to strike the same area at least over several decades, while storms including high-intensity events are more likely to occur frequently over a short time frame in the same area.
Hence, a country affected by a severe earthquake could return to its previous balanced growth path or an improved state over a shorter period than could countries affected
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by storms. For storms, there is a slight chance that recovery efforts as well as investments for damage mitigation will be negatively affected by a similar type of disaster within short time frame. Therefore, recovery and adaptation efforts may be disrupted, and countries affected by storms experience negative growth over the long term. Furthermore, the long-term negative impacts of storms are greater than are short- and medium-term impacts because, as previously mentioned, support from public emergency investments and international financial aid might end after a certain period (Strömberg, 2007).
The results demonstrate the medium- and long-term positive growth effects of NCAT droughts. These results differ from the negative impacts found in previous studies (Loayza et al., 2012; Fomby et al., 2013). This opposite disaster effect may be partially explained by the fact that these previous studies analyzed the impacts of drought in the short term, while our study covers a much longer period. This positive impact is consistent with findings that the farmers in drought-prone areas are more likely to adopt adaptation strategies such as supplementary irrigation and crop switching that are known to increase agricultural productivity (Alauddin and Sarker, 2014; Kurukulasuriya and Mendelsohn, 2008).
2.4.3 Varying disaster effects by economic development
Table 2.6 presents results of the impacts of hydro-meteorological and geophysical disasters by four income groups. Given the rarity of CAT disasters, high-income countries do not experience long-term impacts from hydro-metrological disasters and geophysical disasters for all time frames.
There are several results worth noting. In low-income countries, we do not find
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Table 2.6 Estimation Results by Income Level
Income Levels Low Lower Middle Upper Middle High
(1) (2) (3) (4) (5) (6) (7) (8)
Hydro-meteorological Disasters CAT
Mean Effects (t - t-30) -0.050 -0.755** -1.015** -0.707***
(0.137) (0.301) (0.413) (0.164)
Short-run (t - t-5) -0.075 0.032 -0.202** -0.164***
(0.072) (0.115) (0.100) (0.036)
Medium-run (t-6 - t-10) -0.099 0.003 -0.121 -0.129***
(0.067) (0.085) (0.115) (0.048)
Long-run (t-11 - t-30) -0.046 -0.553** -0.750** omitted
(0.111) (0.220) (0.335)
NCAT
Mean Effects (t - t-30) 0.018 0.080** 0.022 -0.033
(0.030) (0.037) (0.033) (0.022)
Short-run (t - t-5) 0.011 0.017 -0.015 -0.012
(0.010) (0.012) (0.015) (0.008)
Medium-run (t-6 - t-10) -0.005 0.020** 0.031** 0.003
(0.012) (0.010) (0.013) (0.007)
Long-run (t-11 - t-30) -0.004 0.04 -0.023 -0.012
(0.036) (0.035) (0.035) (0.015)
Geophysical Disasters CAT
Mean Effects (t - t-30) 0.358 -0.435** 2.163* omitted
(0.417) (0.202) (1.186)
Short-run (t - t-5) 0.145 -0.119** 0.233 omitted
(0.090) (0.053) (0.189)
Medium-run (t-6 - t-10) 0.003 -0.072 0.187 omitted
(0.118) (0.048) (0.190)
Long-run (t-11 - t-30) 0.412 -0.237 1.900** omitted
(0.373) (0.159) (0.907)
NCAT
Mean Effects (t - t-30) 0.057 -0.092** -0.076 -0.074
(0.093) (0.046) (0.082) (0.052)
Short-run (t - t-5) 0.068** -0.018 -0.014 -0.001
(0.030) (0.013) (0.019) (0.015)
Medium-run (t-6 - t-10) 0.017 -0.013 -0.013 0.001
(0.024) (0.013) (0.022) (0.014)
Long-run (t-11 - t-30) -0.049 -0.056 -0.008 -0.053*
(0.061) (0.043) (0.083) (0.031)
Observations 915 915 956 956 589 589 761 761
Number of Countries 65 65 89 89 68 68 50 50
Adjusted R-squared 0.205 0.206 0.339 0.335 0.327 0.333 0.20 0.287
Note: ***, **, * denote significance at the 1%, 5%, and 10% level, respectively. Robust standard errors reported in parentheses. Controls, country and year fixed effects included but not reported.
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any statistically significant disaster impacts on economic growth except for geophysical CAT disasters in the short term. In contrast, all the CAT and NCAT disasters of both disaster categories have statistically significant impacts in lower-middle-income countries; the results show the positive impacts of hydro-metrological NCAT disasters and the negative impacts of other disasters. According to the results in Table 2.4, geophysical CAT and NCAT disasters have no statistical significance without the distinction of the time frames. However, the results of the subsample analysis by income group indicate that depending on the income group, geophysical disasters have statistically significant impacts on economic growth. In upper-middle-income countries, hydro-metrological CAT disasters have negative impacts similar to those in lower-middle-income countries. However, in this income category, geophysical CAT disasters have positive impacts on economic growth. The contrasting impacts of geophysical CAT disasters in upper-middle- and lower-middle-income groups may explain the lack of statistical significance for the coefficient of the geophysical CAT disaster when the sample is not distinguished by income group (See column (3) in Table 2.4). Lastly, in high-income countries, similar to the results for lower-middle and upper-middle-income groups, we find negative impacts of hydro-metrological CAT disasters.
Overall, we consistently find negative impacts from hydro-metrological disasters across the income groups. Additionally, the results indicate that natural disasters have the most significant and robust impacts in lower-middle-income countries.
Furthermore, the positive impacts of hydro-metrological NCAT disasters observed in the aggregate analysis shown in the column (3) of Table 2.4 reflect the impacts in lower-middle-income groups and do not apply to the other income categories.
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2.5 Conclusions
This study provides extensive empirical analyses on the impacts of natural disasters on economic growth by considering variations in the time frame, disaster severity, disaster type, and level of economic development in a country. When we analyze natural disasters only in terms of time frames, we find positive impacts from the disasters in the short and medium term but not in the long term. Severity also plays a significant role in determining disaster impacts. CAT disasters tend to have negative impacts, while we find some statistically significant positive impacts of NCAT disasters depending on disaster type and income level of the country. The findings of greater long-term impacts from severe disasters than of short- and medium-term impacts imply potential imperfect recovery from large-scale disasters after governmental and international assistance ends (Strömberg, 2007).
The results also indicate that the impacts of natural disasters on economic growth depend on disaster type. Even within the commonly used categories and specific types of natural disasters, some disaster categories or specific types of disasters have positive impacts when the severity of disaster is NCAT, with the exception of storms, which have negative impacts on economic growth regardless of severity.
Overall, we find robust long-term negative impacts of CAT disasters on economic growth. Specifically, negative impacts are widely observed for meteorological disasters such as storms and extreme temperature events, which are growing in their intensity and frequency due to climate change. The results of the subsample analyses by income group indicate that disaster impacts differ significantly by the level of economic development; economic growth in lower-middle-income countries is most sensitive to natural disasters, but developed countries also experience negative impacts
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from CAT disasters.
Finally, as Cavallo et al. (2013) noted, “It is important to notice that many of the events that are recorded in the data set do not correspond to the catastrophic notion of natural disaster that one has in mind when thinking about the potential effect of natural disasters on the macroeconomy.” Continuous efforts in the data collection, analysis and examination of the mechanisms by which natural disasters affect economies and development would provide further insights to improve mitigation and adaptation policies as well as recovery efforts.